29th October 2021
Single Cell RNAseq Analysis Workflow
Quality Control
We will now check the quality of the data further:
- Mapping quality
- Cell counts
- Distribution of keys quality metrics
We will then:
- Filter genes with very low expression
- Identify low-quality cells
- Filter and/or mark low quality cells
Quality Control
We will now check the quality of the data further:
- Mapping quality
- Cell counts
- Distribution of keys quality metrics
We will then:
- Filter genes with very low expression
- Identify low-quality cells
- Filter and/or mark low quality cells
Quality Control
Bioconductor R packages:
- scater
- DropletUtils
Orchestrating Single-Cell Analysis with Bioconductor Robert Amezquita, Aaron Lun, Stephanie Hicks, Raphael Gottardo
Read CellRanger outputs into R
Loading a single sample
sample.path <- c(SRR9264343 = "CellRanger_Outputs/SRR9264343/outs/filtered_feature_bc_matrix/") sce <- read10xCounts(sample.path, col.names=TRUE) sce <- read10xCounts(sample.path, col.names=FALSE)
Loading multiple samples
list_of_files <- c(SRR9264343 = "CellRanger_Outputs/SRR9264343/outs/filtered_feature_bc_matrix",
SRR9264344 = "CellRanger_Outputs/SRR9264344/outs/filtered_feature_bc_matrix",
SRR9264347 = "CellRanger_Outputs/SRR9264347/outs/filtered_feature_bc_matrix")
sce <- read10xCounts(sample.path, col.names=TRUE)
\(\rightarrow\) SingleCellExperiment object
The SingleCellExperiment object
The Counts Matrix
The Counts Matrix
counts(sce)
## 36601 x 3094 sparse Matrix of class "dgCMatrix" ## ## ENSG00000175756 1 2 . 1 1 . . 1 . . . 2 1 . 1 2 2 1 . . 1 . 1 3 1 1 2 1 2 2 . 6 . . 1 1 1 . . . 1 3 1 . ...... ## ENSG00000221978 . 2 . . . . . . . . 3 . . . . 1 . . . . . . . . 1 . . . . 1 . . . . . 1 . . . . 1 . . . ...... ## ENSG00000224870 . . . . . . . . . . . . 1 1 . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000242485 1 2 . 1 2 . . 1 . 1 1 4 . . 1 . 1 2 . 2 . 1 2 . . . . . 1 . . 2 2 . . 1 1 1 1 . 1 . 1 . ...... ## ENSG00000272455 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000235098 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000225905 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000205116 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000225285 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000179403 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000215915 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000160072 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000197785 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . ...... ## ENSG00000205090 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000160075 2 1 . . . . 1 3 1 4 2 3 1 . . . 1 1 . 2 2 1 . 4 2 1 1 1 . . 1 2 . . . 1 . . 1 . 1 2 . . ...... ## ENSG00000215014 . . . . . . . . 1 . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000228594 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000286989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000272106 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000197530 . . . . . . . . . . . . . . . 1 . . . . 1 . . . . . . . . . . 1 . . 1 . . . . . . . . . ...... ## ENSG00000189409 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000248333 . 1 . . . . . . 1 . 2 . . . . . . . . . 1 1 . . . . . . . . . . . . . . 1 . 1 . . . . . ...... ## ENSG00000272004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ## .............................. ## ........suppressing 3050 columns and 36556 rows in show(); maybe adjust 'options(max.print= *, width = *)' ## .............................. ## ## ENSG00000285812 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000223823 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000272141 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000205231 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000162571 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000186891 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000186827 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000078808 . . . . . . 1 . . . . . . . . . . . . 2 . . . . . 1 . . . . . 2 . . . . . . . . . . . . ...... ## ENSG00000176022 . . . . 1 . 1 . . 1 . . . . . . 1 . 1 . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000184163 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000260179 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000160087 . 1 . 1 . . . 1 1 1 1 1 . . . 1 1 1 . 1 . . 1 . . . . . 1 . . 2 . . . . . . . . . 1 2 . ...... ## ENSG00000230415 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000162572 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000131584 . . . . . 1 . 2 . . . . . . . . . . . . . 1 . . . 1 . . . . . . . . 1 1 . . . . . . 1 . ...... ## ENSG00000169972 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . ...... ## ENSG00000127054 1 1 . 1 . . . 2 . . 1 1 . . 2 . . . . . . . 1 . . . . . 1 1 . . . . . . . . . . . . 1 . ...... ## ENSG00000240731 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000224051 . 1 . 1 . . . . . . . 1 . . . . . . 1 . . . . . . 1 . . . . . . . . . . . . . . . . . . ...... ## ENSG00000169962 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000107404 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... ## ENSG00000162576 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......
Feature metadata
Feature metadata
rowData(sce)
## DataFrame with 36601 rows and 3 columns ## ID Symbol Type ## <character> <character> <character> ## ENSG00000175756 ENSG00000175756 AURKAIP1 Gene Expression ## ENSG00000221978 ENSG00000221978 CCNL2 Gene Expression ## ENSG00000224870 ENSG00000224870 MRPL20-AS1 Gene Expression ## ENSG00000242485 ENSG00000242485 MRPL20 Gene Expression ## ENSG00000272455 ENSG00000272455 AL391244.2 Gene Expression ## ... ... ... ... ## ENSG00000240731 ENSG00000240731 AL139287.1 Gene Expression ## ENSG00000224051 ENSG00000224051 CPTP Gene Expression ## ENSG00000169962 ENSG00000169962 TAS1R3 Gene Expression ## ENSG00000107404 ENSG00000107404 DVL1 Gene Expression ## ENSG00000162576 ENSG00000162576 MXRA8 Gene Expression
Droplet annotation (Cell metadata)
Droplet annotation (Cell metadata)
colData(sce)
## DataFrame with 3094 rows and 2 columns ## Sample Barcode ## <character> <character> ## 1 SRR9264343 AAACCTGAGACTTTCG-1 ## 2 SRR9264343 AAACCTGGTCTTCAAG-1 ## 3 SRR9264343 AAACCTGGTGCAACTT-1 ## 4 SRR9264343 AAACCTGGTGTTGAGG-1 ## 5 SRR9264343 AAACCTGTCCCAAGTA-1 ## ... ... ... ## 3090 SRR9264343 TTTGGTTTCTTTAGGG-1 ## 3091 SRR9264343 TTTGTCAAGAAACGAG-1 ## 3092 SRR9264343 TTTGTCAAGGACGAAA-1 ## 3093 SRR9264343 TTTGTCACAGGCTCAC-1 ## 3094 SRR9264343 TTTGTCAGTTCGGCAC-1
colnames(counts(sce))
## NULL
Properties of RNAseq data - Number of genes detected per cell
Properties of RNAseq data - Total UMIs
Properties of RNAseq data - Distribution of counts for a gene across cells
Properties of RNAseq data - Distribution of counts for a gene across cells
Remove undetected genes
Although the count matrix has 36601 genes, many of these will not have been detected in any droplet. We can remove these to reduce the size of the count matrix.
undetected_genes <- rowSums(counts(sce)) == 0 sce <- sce[!undetected_genes,] sce
## class: SingleCellExperiment ## dim: 19938 3094 ## metadata(1): Samples ## assays(1): counts ## rownames(19938): ENSG00000175756 ENSG00000221978 ... ENSG00000169962 ENSG00000107404 ## rowData names(3): ID Symbol Type ## colnames: NULL ## colData names(2): Sample Barcode ## reducedDimNames(0): ## mainExpName: NULL ## altExpNames(0):
Quality Control
- Not all of the droplets called as cells by CellRanger will contain good quality cells
- Poor quality droplets will adversely affect downstream analysis
- We can use QC metrics to filter out poor quality droplets:
- Total UMIs (Library size)
- Number of gene detected
- Proportion of UMIs mapping to mitochondrial genes
- Total UMIs (Library size)
Quality Control
- Add gene annotation to identify Mt genes -
AnnotationHub
rowData(sce)
## DataFrame with 36601 rows and 4 columns ## ID Symbol Type Chromosome ## <character> <character> <character> <character> ## ENSG00000175756 ENSG00000175756 AURKAIP1 Gene Expression 1 ## ENSG00000221978 ENSG00000221978 CCNL2 Gene Expression 1 ## ENSG00000224870 ENSG00000224870 MRPL20-AS1 Gene Expression 1 ## ENSG00000242485 ENSG00000242485 MRPL20 Gene Expression 1 ## ENSG00000272455 ENSG00000272455 AL391244.2 Gene Expression 1 ## ... ... ... ... ... ## ENSG00000212907 ENSG00000212907 MT-ND4L Gene Expression MT ## ENSG00000198886 ENSG00000198886 MT-ND4 Gene Expression MT ## ENSG00000198786 ENSG00000198786 MT-ND5 Gene Expression MT ## ENSG00000198695 ENSG00000198695 MT-ND6 Gene Expression MT ## ENSG00000198727 ENSG00000198727 MT-CYB Gene Expression MT
Quality Control
is.mito <- which(rowData(sce)$Chromosome=="MT") sce <- addPerCellQC(sce, subsets = list(Mito = is.mito))
Adds six columns to the droplet annotation:
- sum: total UMI count
- detected: number of features (genes) detected
- subsets_Mito_sum: number of UMIs mapped to mitochondrial transcripts
- subsets_Mito_detected: number of mitochondrial genes detected
- subsets_Mito_percent: percentage of UMIs mapped to mitochondrial transcripts
- total: also the total UMI count
Quality Control
is.mito <- which(rowData(sce)$Chromosome=="MT") sce <- addPerCellQC(sce, subsets = list(Mito = is.mito))
colData(sce)
## DataFrame with 3094 rows and 8 columns ## Sample Barcode sum detected subsets_Mito_sum #### AAACCTGAGACTTTCG-1 SRR9264343 AAACCTGAGACTTTCG-1 6677 2056 292 ## AAACCTGGTCTTCAAG-1 SRR9264343 AAACCTGGTCTTCAAG-1 12064 3177 575 ## AAACCTGGTGCAACTT-1 SRR9264343 AAACCTGGTGCAACTT-1 843 363 428 ## AAACCTGGTGTTGAGG-1 SRR9264343 AAACCTGGTGTTGAGG-1 8175 2570 429 ## AAACCTGTCCCAAGTA-1 SRR9264343 AAACCTGTCCCAAGTA-1 8638 2389 526 ## ... ... ... ... ... ... ## TTTGGTTTCTTTAGGG-1 SRR9264343 TTTGGTTTCTTTAGGG-1 3489 1600 239 ## TTTGTCAAGAAACGAG-1 SRR9264343 TTTGTCAAGAAACGAG-1 7809 2415 548 ## TTTGTCAAGGACGAAA-1 SRR9264343 TTTGTCAAGGACGAAA-1 9486 2589 503 ## TTTGTCACAGGCTCAC-1 SRR9264343 TTTGTCACAGGCTCAC-1 1182 591 224 ## TTTGTCAGTTCGGCAC-1 SRR9264343 TTTGTCAGTTCGGCAC-1 10514 2831 484 ## subsets_Mito_detected subsets_Mito_percent total ## ## AAACCTGAGACTTTCG-1 12 4.37322 6677 ## AAACCTGGTCTTCAAG-1 12 4.76625 12064 ## AAACCTGGTGCAACTT-1 11 50.77106 843 ## AAACCTGGTGTTGAGG-1 12 5.24771 8175 ## AAACCTGTCCCAAGTA-1 13 6.08937 8638 ## ... ... ... ... ## TTTGGTTTCTTTAGGG-1 11 6.85010 3489 ## TTTGTCAAGAAACGAG-1 12 7.01754 7809 ## TTTGTCAAGGACGAAA-1 12 5.30255 9486 ## TTTGTCACAGGCTCAC-1 11 18.95093 1182 ## TTTGTCAGTTCGGCAC-1 12 4.60339 10514
QC metrics - distribution
plotColData(sce, x="Sample", y="sum") + scale_y_log10() plotColData(sce, x="Sample", y="detected") + scale_y_log10() plotColData(sce, x="Sample", y="subsets_Mito_percent")
QC metrics - relationship
Identification of low-quality cells with adaptive thresholds
sce$low_lib_size <- isOutlier(sce$sum, log=TRUE, type="lower") sce$low_n_features <- isOutlier(sce$detected, log=TRUE, type="lower") sce$high_Mito_percent <- isOutlier(sce$subsets_Mito_percent, type="higher")
All three filter steps at once
cell_qc_results <- quickPerCellQC(colData(sce), percent_subsets=c("subsets_Mito_percent"))
## DataFrame with 3094 rows and 4 columns ## low_lib_size low_n_features high_subsets_Mito_percent discard ## <outlier.filter> <outlier.filter> <outlier.filter> <logical> ## 1 FALSE FALSE FALSE FALSE ## 2 FALSE FALSE FALSE FALSE ## 3 TRUE TRUE TRUE TRUE ## 4 FALSE FALSE FALSE FALSE ## 5 FALSE FALSE FALSE FALSE ## ... ... ... ... ... ## 3090 FALSE FALSE FALSE FALSE ## 3091 FALSE FALSE FALSE FALSE ## 3092 FALSE FALSE FALSE FALSE ## 3093 FALSE TRUE TRUE TRUE ## 3094 FALSE FALSE FALSE FALSE
All three filter steps at once
cell_qc_results <- quickPerCellQC(colData(sce), percent_subsets=c("subsets_Mito_percent"))
Filter the Single Cell Object
- Filter cells according to QC metrics.
sce.Filtered <- sce[, !cell_qc_results$discard] sce.Filtered
## class: SingleCellExperiment ## dim: 36601 2730 ## metadata(1): Samples ## assays(1): counts ## rownames(36601): ENSG00000175756 ENSG00000221978 ... ENSG00000107404 ENSG00000162576 ## rowData names(4): ID Symbol Type Chromosome ## colnames(2730): AAACCTGAGACTTTCG-1 AAACCTGGTCTTCAAG-1 ... TTTGTCAAGGACGAAA-1 ## TTTGTCAGTTCGGCAC-1 ## colData names(8): Sample Barcode ... subsets_Mito_percent total ## reducedDimNames(0): ## mainExpName: NULL ## altExpNames(0):